The CHOVE-CHUVA project is a Space for Climate Observatory initiative aiming at developing operational tools to monitor socio-environmental dynamics in the Brazilian state of Mato Grosso, in the Southern Amazon. This project focuses on the dissemination of remote sensing-based spatial information to monitor the evolution of climate variables and land use dynamics, especially regarding agriculture, natural vegetation and hydrological resources. The platform is enhanced by the collection of collaborative data about the adoption of specific land use types (e.g. forest restoration and crop-livestock-forest integrated systems) encouraged by the Brazilian program for a low-carbon agriculture (ABC plan).

The rapid expansion of a very active agricultural frontier in the Southern Brazilian Amazon induces high deforestation rates that influence local to global water cycles. In this study, we analysed landscape indicators derived from Brazilian MapBiomas land use maps in order to assess deforestation patterns in watersheds dominated by crop or pasture lands. Our results indicate that the proportion of forest is on average higher in watersheds where pasture prevails (53,7%) compared to watersheds where soybean prevails (42,4%). On the contrary, we also found that riparian buffers in soybean dominant watersheds are better preserved (75,7% covered by forest) than in pasture dominant watersheds (60,0% of forested area). Finally, we emphasize the benefit of monitoring land use impacts on stream reach scale, as new remote sensing technologies are under development.

Monitoring forest degradation at fine scale over large area is critical from an environmental point of view since it provides crucial information for many ecological applications. We introduce an automatic method based on optical Landsat time series (2000–2017) to detect and quantify forest disturbances and to identify the causes of forest degradation. The method is based on i) an automatic spectral unmixing to detect forest's disturbances and on ii) landscape metrics and temporal indicators to detect the causes of forest degradation. We applied the approach in the Brazilian Amazon municipality of Paragominas to map forested areas affected by reduced impact logging, conventional logging or illegal logging and fires.

Radar altimetry is commonly used to derive water levels over inland water bodies. If lakes and rivers are increasingly covered with radar altimetry virtual stations, wetlands and floodplains are still poorly monitored using this technique. In this study, an unsupervised classification of Ku-band radar altimetry backscattering coefficients from ENVISAT and Jason-2 is performed in the Congo Cuvette Centrale. Comparisons performed against a classification map of the study area shows a good agreement between the water and vegetation classes of the two datasets. Based on these results, radar altimetry-derived water levels are automatically derived over the water classes. Comparisons against radar altimetry-based water stages from Hydroweb database exhibits also a good agreement.

The Lençóis Maranhenses National Park (LMNP) constitutes the largest coastal dune field in South America. It is a remarkable reservoir of biodiversity facing important preservation challenges due to the rapid development of anthropogenic activities, including tourism. The objective of this study is to introduce preliminary results on the understanding of seasonal sand dune dynamics. For this purpose, we used Sentinel 2 time series from 29/07/17 to 17/09/18 in order to monitor the intra-annual migration of interdunal ponds. The method relied on three steps: 1) automatic Spectral Mixture Analysis to estimate the proportion of vegetation, mineral and water in each pixel, 2) rule-based classification and 3) extraction of interdunal areas to assess their dynamics. The average offset distance was 21.6 meters and the angle was 72.6°, corresponding to the main west-southwest wind direction. Additional studies to assess the long term expansion of the dune field are necessary.

Land cover and land use monitoring, particularly during winter season, is still a major environmental challenge. Indeed, the presence of a vegetation cover, the dates of sowing, the length of the intercrop period, and land use types have an impact on pollutant transport to water bodies. The objective of this study was to evaluate the potentiality of polarimetric C- and L-SAR time-series to improve the identification and characterization of vegetation cover during winter season in a 130 km2area. Alos-2, Radarsat-2 and Sentinel-1 time-series were classified using RF algorithm. The best results were obtained from Radarsat-2 polarimetric images acquired between August 2016 and May 2017, with an overall accuracy of ~ 80%.

Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km2. For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images.

The aim of this article is to evaluate the potential of optical and multi-polarization SAR images for soybean yield estimation by their assimilations into a simple agro-meteorological model. Satellite and ground data were acquired over two sites during the MCM'10 experiment. Optical and radar images were provided by Formosat-2, Spot-4, Spot-5 and Radarsat-2 satellites during the whole vegetation cycle of soybean. Results show that the assimilation of optical or SAR offer similar performances for the estimation of crop parameters (i.e. LAI and dry biomass) and crop yield (rRMSE = 18% in the worst case). Concerning SAR data, results highlighted the interest of using backscattering coefficients acquired at VV polarization (rRMSE = 2%).

The objective of this study was to evaluate how the combined use of multi-temporal optical and radar data can improve the precision of crop estimation in taking into account both discontinuous information on green vegetation and continuous information on vegetation cover.